MAULANA ABUL KALAM AZAD UNIVERSITY OF ...makautexam.net/aicte_details/SyllabusI/BSCITBDA/sem2.pdfThe HDFS file system, MapReduce frameworks are studied in detail. 2. Hadoop tools like

MAULANA ABUL KALAM AZAD UNIVERSITY OF TECHNOLOGY, WEST BENGAL NH-12 (Old NH-34), Simhat, Haringhata, Nadia -741249

Department of Information Technology Syllabus of B.Sc. in Information Technology (Big Data Analytics)

(Effective from academic session 2019-20)

Page 1 of 26

Semester-II

Name of the Course: B.Sc. in Information Technology (Big Data Analytics) Subject: Data Acquisition & Processing, Data Acquisition & Processing Lab Course Code: BITDBA201 & BITDBA291

Semester: II

Duration: 36 Hrs. Maximum Marks: 100+100 Teaching Scheme Examination Scheme Theory: 3 hrs./week End Semester Exam: 70 Tutorial: 0 Attendance : 5 Practical:4 hrs./week Continuous Assessment:25 Credit: 3+2 Practical Sessional internal continuous evaluation:40 Practical Sessional external examination:60 Aim: Sl. No.

1. Understand the principles of operation and limitations of common measuring instruments.

2. Model instruments and their operating conditions to use the instruments correctly. 3. Design systems for the acquisition, analysis, and communication of data 4. Gain awareness of economical and societal aspects of instrumentation systems and

communication of data. Objective: Sl. No.

1. To understand concepts of acquiring the data from transducers/input devices, their interfacing and instrumentation system design.

2. To familiarize with different data transfer techniques. 3. To automate the acquisition and processing of data.

Pre-Requisite: Sl. No.

1. Electrical and Electronics subject knowledge 2. Mathematical knowledge

Contents Hrs./week Chapter Name of the Topic Hours Marks 01

Sensors: temperature, light, displacement, acceleration, pressure, flow, mechanical strain.

3 5

02 Data acquisition: pre-processing and filtering, impedance matching, band pass of the measurement system. 3 8

03

AD/DA converters: AD and DA techniques, data acquisition 3 8




Page 2 of 26

systems, convertor properties, the selection and use of ADC.

04 Basics of microcontrollers: properties, block diagram, input and

output units, timing units, other peripheral units. 3 5

05 Personal computer: sound card, RS232, RS422, GPIB, PCI, USB. 6 7

06 Acquisition: sampling, Nyquist criteria, frequency aliasing. 6 10

07

Basics of digital data processing: FFT, digital filtering, convolution, FIR, IIR.

6 10

08 Applications in data processing: modulation and demodulation (AM, FM, PM), measurement (amplitude, phase, frequency, period), oscillators.

3 10

09

Basics of programmable logic circuits: CPLD and PFHA architecture, examples of the use, basics of programming language VHDL.

3 7

Sub Total: 36 70

Internal Assessment Examination & Preparation of Semester Examination

4 30

Total: 40 100 Practical: Skills to be developed: Intellectual skills:

1. Can offer and explain what has been created in a way others can understand and see the nature / unique / specifics of it.

2. Can distinguish which ideas could prove correct. 3. Use an idea to create something new and original which works better than

the original. List of Practical: Based on Theory Assignments: Based on the curriculum as covered by subject teacher. List of Books Text Books:

Name of Author Title of the Book Edition/ISSN/ISBN Name of the Publisher




Page 3 of 26

S.W. Smith The Scientist and Engineer's Guide to

Digital Signal processing

California Technical Publishing

Reference Books:

W.J. Thompson, J.G. Webster

Interfacing Sensors to the IBM PC

Prentice Hall

A. Bateman, I. Paterson-Stephens

The DSP Handbook Prentice Hall

List of equipment/apparatus for laboratory experiments: Sl. No. Sensor, DAQ Device

1. Computer 2.

End Semester Examination Scheme. Maximum Marks-70. Time allotted-3hrs. Group Unit Objective Questions

(MCQ only with the correct answer)

Subjective Questions

No of question to be set

Total Marks

No of question to be set

To answer

Marks per question

Total Marks

A B C

1 to 9 1 to 9 1 to 9

10

10

5 5

3 3

5 15

60

Only multiple choice type question (MCQ) with one correct answer are to be set in the objective part.

Specific instruction to the students to maintain the order in answering objective questions should be given on top of the question paper.

Examination Scheme for end semester examination: Group Chapter Marks of each

question Question to be set

Question to be answered

A All 1 10 10 B All 5 5 3 C All 15 3 3 Examination Scheme for Practical Sessional examination: Practical Internal Sessional Continuous Evaluation Internal Examination: Continuous evaluation 40 External Examination: Examiner- Signed Lab Note Book 10 On Spot Experiment 40 Viva voce 10 60




Page 4 of 26

Name of the Course: B.Sc. in Information Technology (Big Data Analytics)

Subject: Foundation in Big Data Analysis and Hadoop Lab

Course Code: BITBDA202 &

BITBDA292

Semester: II

Duration: 36 Hrs Maximum Marks:100+100

Teaching Scheme Examination Scheme

Theory: 3 hrs./week End Semester Exam:70

Tutorial: 0 Attendance: 5

Practical: 4 hrs./week Continuous Assessment: 25

Credit: 3+2 Practical Sessional internal continuous evaluation: 40

Practical Sessional external examination: 60

Aim:

Sl. No.

1. Understand big data for business intelligence

2. Learn business case studies for big data analytics.

3. Understand nosql big data management.

4. Perform map-reduce analytics using Hadoop and related tools

Objective:

Sl. No.

1. Understand the fundamentals of Big cloud and data architectures.

2. Understand HDFS file structure and Mapreduce frameworks, and use them to solve

complex problems, which require massive computation power

3. Use relational data in a Hadoop environment, using Hive and Hbase tools of the Hadoop

Ecosystem..

4. Understand the Comparison with traditional databases.




Page 5 of 26

Pre-Requisite:

Sl. No.

1. Database Management Systems.

2. Object Oriented Programming Through Java

Contents 3 Hrs./week

Chapter Name of the Topic Hours Marks

01 Introduction to big data

Introduction to Big Data Platform – Challenges of Conventional

Systems - Intelligent data analysis – Nature of Data - Analytic

Processes and Tools - Analysis vs Reporting.

6 10

02 Mining data streams

Introduction To Streams Concepts – Stream Data Model and

Architecture - Stream Computing - Sampling Data in a Stream –

Filtering Streams – Counting Distinct Elements in a Stream –

Estimating Moments – Counting Oneness in a Window – Decaying

Window - Real time Analytics Platform(RTAP) Applications - Case

Studies - Real Time Sentiment Analysis- Stock Market Predictions.

10 20

03 Hadoop

History of Hadoop- the Hadoop Distributed File System –

Components of Hadoop Analysing the Data with Hadoop- Scaling

Out- Hadoop Streaming- Design of HDFS-Java interfaces to HDFS

Basics- Developing a Map Reduce Application-How Map Reduce

Works-Anatomy of a Map Reduce Job run-Failures-Job

Scheduling-Shuffle and Sort – Task execution - Map Reduce Types

and Formats- Map Reduce FeaturesHadoop environment.

12 20

04 Frameworks

Applications on Big Data Using Pig and Hive – Data processing

operators in Pig – Hive services – HiveQL – Querying Data in Hive

- fundamentals of HBase and ZooKeeper - IBM InfoSphere

8 20




Page 6 of 26

BigInsights and Streams. Predictive Analytics- Simple linear

regression- Multiple linear regression- Interpretation 5 of

regression coefficients. Visualizations - Visual data analysis

techniques- interaction techniques - Systems and applications.

Sub Total: 36 70

Internal Assessment Examination & Preparation of Semester

Examination

4 30

Total: 40 100

Practical:

Skills to be developed:

Intellectual skills:

1. The HDFS file system, MapReduce frameworks are studied in detail.

2. Hadoop tools like Hive, and Hbase, which provide interface to relational databases, are also covered as part of this course work.

3. Ability to implement algorithms to perform various operations on Mapreduce,Pig,Hive

List of Practical:

1. Basic Linux command

2. Installation of Hadoop .

3. Create a directory in HDFS at given path(s).

4. Copy a file from/To Local file system to HDFS

5. Remove a file or directory in HDFS.

6. Display the aggregate length of a file.

7. Word Count Map Reduce program to understand Map Reduce Paradigm

8. Implementing Matrix Multiplication with Hadoop Map Reduce

9. Pig Latin scripts to sort,group, join,project, and filter your data.

10. Hive Databases,Tables,Views,Functions and Indexes




Page 7 of 26

Assignments:

Based on the curriculum as covered by subject teacher.

List of Books

Text Books:

Name of Author Title of the Book Edition/ISSN/ISBN Name of the

Publisher

Tom White Hadoop: The Definitive

Guide

Third Edition O’reilly Media

Chris Eaton, Dirk

DeRoos, Tom Deutsch,

George Lapis, Paul

Zikopoulos

Understanding Big

Data: Analytics for

Enterprise Class

Hadoop and Streaming

Data

McGrawHill

Publishing

Reference Books:

Anand Rajaraman and

Jeffrey David Ullman

Mining of Massive

Datasets

CUP

Bill Franks Taming the Big Data

Tidal Wave: Finding

Opportunities in Huge

Data Streams with

Advanced Analytics

John Wiley& sons

Glenn J. Myatt Making Sense of Data John Wiley & Sons

Pete Warden Big Data Glossary O’Reilly

List of equipment/apparatus for laboratory experiments:

Sl. No.

1. Computer with moderate configuration

2. Linux os or VM




Page 8 of 26

3. Hadoop 2.x or higher and other software as required.

End Semester Examination Scheme. Maximum Marks-70. Time allotted-3hrs.

Group Unit Objective Questions

(MCQ only with the

correct answer)


No of

question

to be set

Total

Marks

No of

question

to be set

To

answer

Marks per

question

Total

Marks

A

B

C

1 to 4

1 to 4

1 to 4

10 10

5

5

3

3

5

15

60

● Only multiple choice type question (MCQ) with one correct answer are to be set in the

objective part.

● Specific instruction to the students to maintain the order in answering objective questions

should be given on top of the question paper.

Examination Scheme for end semester examination:

Group Chapter Marks of each

question

Question to be

set

Question to be

answered

A All 1 10 10

B All 5 5 3

C All 15 5 3

Examination Scheme for Practical Sessional examination:

Practical Internal Sessional Continuous Evaluation

Internal Examination:




Page 9 of 26

Continuous evaluation 40

External Examination: Examiner-

Signed Lab Note Book 10

On Spot Experiment 40

Viva voce 10 60




Page 10 of 26


Subject: Data Structure and Algorithm with Python & Data Structure and Algorithm with Python lab

Course Code: BITBDA203 &

BITBDA293

Semester: II

Duration: 36 Hrs Maximum Marks:100+100


Theory: 3 hrs./week End Semester Exam:70


Practical: 4 hrs./week Continuous Assessment: 25

Credit: 3+2 Practical Sessional internal continuous evaluation: 40


Aim:

Sl. No.

1. The point of this course is to give you a vibe for algorithms and data structures as

a focal area of what it is to be a computer science student.

2. You ought to know about the way that there are regularly a few calculations for

some issue, and one calculation might be superior to another, or one calculation

better in certain conditions and another better in others.

3. You should have some idea of how to work out the efficiency of an algorithm.

4. You will be able to use and design linked data structures

5. You will learn why it is good programming style to hide the details of a data

structure within an abstract data type.

6. You should have some idea of how to implement various algorithm using python

programming.

Objective:

Sl. No.




Page 11 of 26

1. To impart the basic concepts of data structures and algorithms.

2. To understand concepts about searching and sorting techniques.

3. To understand basic concepts about stacks,queues,lists,trees and graphs.

4. To understanding about writing algorithms and step by step approach in solving

problems with the help of fundamental data structures

Pre-Requisite:

Sl. No.

1. Basics of programming language.

2. Logic building skills.



01 Introduction to Data Structure

Abstract Data Type.

1 2

02 Arrays

1D, 2D and Multi-dimensional Arrays, Sparse Matrices.Polynomial

representation .

3 4

03 Linked Lists

Singly, Doubly and Circular Lists, Normal and Circular

representation of Self Organizing Lists, Skip Lists,

Polynomial representation.

4 7

04 Stacks

Implementing single / multiple stack/s in an Array, Prefix, Infix

and Postfix expressions, Utility and conversion of these

expressions from one to another, Applications of stack, Limitations

of Array representation of stack.

4 10




Page 12 of 26

05 Queues

Array and Linked representation of Queue, Circular Queue, De-

queue, Priority Queues.

4 7

06 Recursion

Developing Recursive Definition of Simple Problems and their

implementation, Advantages and Limitations of Recursion,

Understanding what goes behind Recursion (Internal Stack

Implementation)

4 5

07 Trees

Introduction to Tree as a data structure, Binary Trees (Insertion,

Deletion, Recursive and Iterative Traversals of Binary Search

Trees), Threaded Binary Trees (Insertion, Deletion, Traversals),

Height-Balanced Trees (Various operations on AVL Trees).

5 15

08 Searching and Sorting

Linear Search, Binary Search, Comparison of Linear and Binary

Search, Selection Sort, Insertion Sort, Merge Sort, Quick sort, Shell

Sort, Comparison of Sorting Techniques

6 15

09 Hashing

Introduction to Hashing, Deleting from Hash Table, Efficiency of

Rehash Methods, Hash Table Reordering, Resolving collision by

Open Addressing, Coalesced Hashing, Separate Chaining, Dynamic

and Extendible Hashing, Choosing a Hash Function, Perfect

Hashing Function.

5 5

Sub Total: 36 70


Examination

4 30

Total: 40 100

Practical:

Skills to be developed:




Page 13 of 26

Intellectual skills:

1. Skill to analyze algorithms and to determine algorithm correctness and their time efficiency.

2. Knowledge of advanced abstract data type (ADT) and data structures and their implementations.

3. Ability to implement algorithms to perform various operations on data structures.

List of Practical:

1. Implementation of array operations.

2. Stacks and Queues: adding, deleting elements .

3. Circular Queue: Adding & deleting elements

4. Merging Problem : Evaluation of expressions operations on Multiple stacks & queues

5. Implementation of linked lists: inserting, deleting, inverting a linked list.

6. Implementation of stacks & queues using linked lists:

7. Polynomial addition, Polynomial multiplication

8. Sparse Matrices : Multiplication, addition.

9. Recursive and Non Recursive traversal of Trees Threaded binary tree traversal.AVL tree implementation Application of Trees.

10. Application of sorting and searching algorithms Hash tables implementation: searching, inserting and deleting, searching & sorting techniques.

Assignments:


List of Books

Text Books:


Publisher

Michael H. Goldwasser,

Michael T. Goodrich,

Data Structures and

Algorithms in Python

1118476735,

9781118476734

John Wiley & Sons




Page 14 of 26

and Roberto Tamassia

Rance D Necaise Data Structures and

Algorithms Using

Python

9788126562169 John Wiley & Sons

Reference Books:

Sartaj Sahni DataStructures,

Algorithms and

applications in C++

Second Edition Universities Press

List of equipment/apparatus for laboratory experiments:

Sl. No.

4. Computer with moderate configuration

5. Python 2.7 or higher and other softwares as required.



(MCQ only with the

correct answer)


No of

question

to be set

Total

Marks

No of

question

to be set

To

answer

Marks per

question

Total

Marks

A

B

C

1 to 9

1 to 9

1 to 9

10 10

5

5

3

3

5

15

60


objective part.




Page 15 of 26





question

Question to be

set

Question to be

answered

A All 1 10 10

B All 5 5 3

C All 15 5 3

Examination Scheme for Practical Sessional examination:

Practical Internal Sessional Continuous Evaluation

Internal Examination:

Continuous evaluation 40

External Examination: Examiner-

Signed Lab Note Book 10

On Spot Experiment 40

Viva voce 10 60




Page 16 of 26


Subject: Discrete Mathematics

Course Code: BITBDA204 Semester: II

Duration: 48 Hrs Maximum Marks: 100


Theory: 3 Hrs./week End Semester Exam: 70

Tutorial:1 Hrs./week Attendance: 5

Practical: 0 Continuous Assessment: 25

Credit:4 Practical Sessional internal continuous evaluation: NA

Practical Sessional external examination: NA

Aim:

Sl. No.

1. The aim of this course is to introduce you with a new branch of mathematics

which is discrete mathematics, the backbone of Computer Science.

2. In order to be able to formulate what a computer system is supposed to do, or to

prove that it does meet its specification, or to reason about its efficiency, one

needs the precision of mathematical notation and techniques. The Discrete

Mathematics course aims to provide this mathematical background.

Objective: Throughout the course, students will be expected to demonstrate their

understanding of

Discrete Mathematics by being able to do each of the following

Sl. No.

1. Use mathematically correct terminology and notation.

2. Construct correct direct and indirect proofs.




Page 17 of 26

3. Use division into cases in a proof.

4. Use counterexamples.

5. Apply logical reasoning to solve a variety of problems.

Pre-Requisite:

Sl. No.

1. Knowledge of basic algebra

2. Ability to follow logical arguments.



01 Set Theory

Definition of Sets, Venn Diagrams, complements, Cartesian

products, power sets, counting principle, cardinality and

countability (Countable and Uncountable sets), proofs of some

general identities on sets, pigeonhole principle. Relation:

Definition, types of relation, composition of relations, domain and

range of a relation, pictorial representation of relation, properties

of relation, partial ordering relation. Function: Definition and types

of function, composition of functions, recursively defined

functions.

10 14

02 Propositional logic

Proposition logic, basic logic, logical connectives, truth tables,

tautologies, contradictions, normal forms (conjunctive and

disjunctive), modus ponens and modus tollens, validity, predicate

logic, universal and existential quantification. Notion of proof:

proof by implication, converse, inverse, contrapositive, negation,

and contradiction, direct proof, proof by using truth table, proof by

counter example.

10 14

03 Combinatorics

Mathematical induction, recursive mathematical definitions, basics

10 14




Page 18 of 26

of counting, permutations, combinations, inclusion-exclusion,

recurrence relations (nth order recurrence relation with constant

coefficients, Homogeneous recurrence relations, Inhomogeneous

recurrence relation), generating function (closed form expression,

properties of G.F., solution of recurrence relation using G.F,

solution of combinatorial problem using G.F.)

04 Algebraic Structure

Binary composition and its properties definition of algebraic

structure, Groyas Semi group, Monoid Groups, Abelian Group,

properties of groups, Permutation Groups, Sub Group, Cyclic

Group, Rings and Fields (definition and standard results).

8 10

05 Graphs

Graph terminology, types of graph connected graphs, components

of graph, Euler graph, Hamiltonian path and circuits, Graph

coloring, Chromatic number. Tree: Definition, types of tree(rooted,

binary), properties of trees, binary search tree, tree traversing

(preorder, inorder, post order). Finite Automata: Basic concepts of

Automation theory, Deterministic finite Automation (DFA),

transition function, transition table, Non Deterministic Finite

Automata (NDFA), Mealy and Moore Machine, Minimization of

finite Automation.

10 18

Sub Total: 48 70


Examination

4 30

Total: 52 100

Assignments:


List of Books

Text Books:





Page 19 of 26

Publisher

Kenneth H. Rosen Discrete Mathematics

and its Applications

Tata Mc.Graw Hill

eymourLipschutz,

M.Lipson

Discrete Mathematics Tata Mc.Graw Hill

Reference Books:

V. Krishnamurthy Combinatorics:Theory

and Applications

East-West Press

Kolman, Busby Ross Discrete Mathematical

Structures

Prentice Hall

International



(MCQ only with the

correct answer)


No of

question

to be set

Total

Marks

No of

question

to be set

To

answer

Marks per

question

Total

Marks

A

B

C

1 to 5

1 to 5

1 to 5

10 10

5

5

3

3

5

15

60


objective part.







Page 20 of 26


question

Question to be

set

Question to be

answered

A All 1 10 10

B All 5 5 3

C All 15 5 3




Page 21 of 26


Subject: Environmental Science


Duration: 36 Hrs Maximum Marks: 100


Theory: 1 Hrs./week End Semester Exam: 70


Practical: 0 Continuous Assessment: 25

Credit: 1 Practical Sessional internal continuous evaluation: NA

Practical Sessional external examination: NA

Aim:

Sl. No.

1. To enable critical thinking in relation to environmental affairs.

2. Understanding about interdisciplinary nature of environmental issues

3. Independent research regarding environmental problems in form of project report

Objective:

Sl. No.

1. To create awareness about environmental issues.

2. To nurture the curiosity of students particularly in relation to natural environment.

3. To develop an attitude among students to actively participate in all the activities

regarding environment protection

4. To develop an attitude among students to actively participate in all the activities

regarding environment protection





Page 22 of 26


01 Introduction

Basic ideas of environment, basic concepts, man, society &amp,

environment, their interrelationship. Mathematics of population

growth and associated problems, Importance of population study

in environmental engineering, definition of resource, types of

resource, renewable, non- renewable, potentially renewable, effect

of excessive use vis-à-vis population growth, Sustainable

Development.

Materials balance: Steady state conservation system, steady state

system with non-conservative pollutants, step function.

Environmental degradation: Natural environmental Hazards like

Flood, earthquake, Landslide-causes, effects and

control/management, Anthropogenic degradation like Acid rain-

cause, effects and control. Nature and scope of Environmental

Science and Engineering.

3 10

02 Ecology

Elements of ecology: System, open system, closed system,

definition of ecology, species, population, community, definition of

ecosystem- components types and function.

Structure and function of the following ecosystem: Forest

ecosystem, Grassland ecosystem, Desert ecosystem, Aquatic

ecosystems, Mangrove ecosystem (special reference to Sundar

ban), Food chain [definition and one example of each food chain],

Food web.

Biogeochemical Cycle- definition, significance, flow chart of

different cycles with only elementary reaction [Oxygen, carbon,

Nitrogen, Phosphate, Sulphur].

Biodiversity- types, importance, Endemic species, Biodiversity Hot-

spot, Threats to biodiversity, Conservation of biodiversity.

7 10

03 Air pollution and control

Atmospheric Composition: Troposphere, Stratosphere,

6 10




Page 23 of 26

Mesosphere, Thermosphere,Tropopause and Mesopause. Energy

balance:Conductive and Convective heat transfer, radiation heat

transfer, simple global temperature model [Earth as a black body,

earth as albedo], Problems.Green house effects: Definition, impact

of greenhouse gases on the global climate and consequently on sea

water level, agriculture and marine food.Global warming and its

consequence, Control of Global warming. Earth’s heat budget.

Lapse rate: Ambient lapse rate Adiabatic lapse rate, atmospheric

stability, temperature inversion (radiation inversion). Atmospheric

dispersion: Maximum mixing depth, ventilation coefficient,

effective stack height, smokestack plumes and Gaussian plume

model. Definition of pollutants and contaminants, Primary and

secondary pollutants: emission standard, criteria pollutant.

Sources and effect of different air pollutants- Suspended

particulate matter, oxides of carbon, oxides of nitrogen, oxides of

sulphur, particulate, PAN. Smog, Photochemical smog and London

smog. Depletion Ozone layer: CFC, destruction of ozone layer by

CFC, impact of other green house gases, effect of ozone

modification. Standards and control measures: Industrial,

commercial and residential air quality standard, control measure

(ESP. cyclone separator, bag house, catalytic converter, scrubber

(ventury), Statement with brief reference).

04 Water Pollution and Control

Hydrosphere, Hydrological cycle and Natural water. Pollutants of

water, their origin and effects: Oxygen demanding wastes,

pathogens, nutrients, Salts, thermal application, heavy metals,

pesticides, volatile organic compounds. River/Lake/ground water

pollution: River: DO, 5 day BOD test, Seeded BOD test, BOD

reaction rate constants, Effect of oxygen demanding wastes on

river[deoxygenation, reaeration], COD, Oil, Greases, pH. Lake:

Eutrophication [Definition, source and effect]. Ground water:

Aquifers, hydraulic gradient, ground water flow (Definition only)

Standard and control: Waste water standard [BOD, COD, Oil,

Grease], Water Treatment system [coagulation and flocculation,

sedimentation and filtration, disinfection, hardness and alkalinity,

softening] Wastewater treatment system, primary and secondary

treatments [Trickling filters, rotating biological contractor,

Activated sludge, sludge treatment, oxidation ponds] tertiary

treatment definition. Water pollution due to the toxic elements and

6 15




Page 24 of 26

their biochemical effects: Lead, Mercury, Cadmium, and Arsenic.

05 Land Pollution

Lithosphere, Internal structure of earth, rock and soil 1L Solid

Waste: Municipal, industrial,

commercial, agricultural, domestic, pathological and hazardous

solid wastes, Recovery and

disposal method- Open dumping, Land filling, incineration,

composting, recycling. Solid

waste management and control (hazardous and biomedical waste).

4 10

06 Noise Pollution

Definition of noise, effect of noise pollution, noise classification

[Transport noise, occupational noise, neighbourhood noise]

Definition of noise frequency, noise pressure, noise intensity, noise

threshold limit value, equivalent noise level,(18hr Index), Ldn.

Noise pollution control.

5 10

07 Environmental Management

Environmental impact assessment, Environmental Audit,

Environmental laws and protection act of India, Different

international environmental treaty/ agreement/ protocol.

5 5

Sub Total: 36 70


Examination

4 30

Total: 40 100


Publisher

G. M.Masters, Introduction to

Environmental

Engineering and

Science

Prentice-Hall of India

Pvt. Ltd., 1991




Page 25 of 26

Reference Books:

A. K. De Environmental

Chemistry

New Age International



(MCQ only with the

correct answer)


No of

question

to be set

Total

Marks

No of

question

to be set

To

answer

Marks per

question

Total

Marks

A

B

C

1 to 5

1 to 5

1 to 5

10 10

5

5

3

3

5

15

60

● Only multiple choice type question (MCQ) with one correct answer are to be set in the objective part.

● Specific instruction to the students to maintain the order in answering objective questions should be given on top of the question paper.



question

Question to be

set

Question to be

answered

A All 1 10 10

B All 5 5 3

C All 15 5 3




Page 26 of 26


Subject: Project I


Duration: 36 Hrs. Maximum Marks: 100


Theory: 0 End Semester Exam: 100


Practical: 2 Hrs./week Continuous Assessment: 0

Credit: 1 Practical Sessional internal continuous evaluation: 40


Contents

Students will do projects on application areas of latest technologies and current topics of societal relevance.

Documents

MAULANA ABUL KALAM AZAD UNIVERSITY OF ...makautexam.net/aicte_details/SyllabusI/BSCITBDA/sem2.pdfThe HDFS file system, MapReduce frameworks are studied in detail. 2. Hadoop tools like